Second primary malignancy (SPM) ranks the second leading cause of death in patients with head and neck cancer (HNC), while studies exploring the risk factors for SPM are limited. To clarify this, we investigated the relationship between the chemotherapy and SPM using the Surveillance, Epidemiology, and End Results (SEER) database. 11 345 patients initially diagnosed with HNC between 1998 and 2016 were selected from the SEER database. First, these patients were divided into two groups according to chemotherapy or not. With Fine and Gray model, the subdistribution hazard ratio (sHR) of chemotherapy was calculated based on Propensity Score Matching (PSM). Second, the 11 345 cases were randomized into a training set and a validation set. Based on the training set, the different cumulative incidence of SPMs between the patients with and without chemotherapy was estimated respectively in the high‐ and low‐risk group according to the scores derived from a nomogram. Chemotherapy was negatively correlated to the SPMs (sHR: 0.847, 95% CI: 0.733‐0.977, P = .023) by conducting competing risk analysis. With chemotherapy, forest plots showed subgroups of squamous cell carcinoma (SCC, sHR: 0.815, 95% CI: 0.7‐0.948, P = .008), 50‐64 years old (sHR:0.794, 95% CI: 0.655‐0.962, P = .019), male (sHR:0.828, 95% CI: 0.703‐0.974, P = .023), and well/moderate histological grade (sHR:0.828, 95% CI: 0.688‐0.996, P = .045) were negatively correlated to SPMs; the nomogram showed the high‐risk population characterized as SCC, elder age, male, and well/moderate histological grade also tended to have lower incidence of SPMs (sHR: 0.805, 95% CI: 0.669‐0.969, P = .022). Despite HNC patients with characteristics of SCC, increased age, male, and well/moderate histological grade had higher risk of a SPM, they were also more likely to be benefitted from chemotherapy to avoid it.

Breast cancer leads to most of cancer deaths among women worldwide. Systematically analyzing the competing endogenous RNA (ceRNA) network and their functional modules may provide valuable insight into the pathogenesis of breast cancer. In this study, we constructed a lncRNA-TF-associated ceRNA network via combining all the significant lncRNA-TF ceRNA pairs and TF-TF PPI pairs. We computed important topological features of the network, such as degree and average path length. Hub nodes in the lncRNA-TF-associated ceRNA network were extracted to detect differential expression in different subtypes and tumor stages of breast cancer. MCODE was used for identifying the closely connected modules from the ceRNA network. Survival analysis was further used for evaluating whether the modules had prognosis effects on breast cancer. TF motif searching analysis was performed for investigating the binding potentials between lncRNAs and TFs. As a result, a lncRNA-TF-associated ceRNA network in breast cancer was constructed, which had a scale-free property. Hub nodes such as MDM4, ZNF410, AC0842-19, and CTB-89H12 were differentially expressed between cancer and normal sample in different subtypes and tumor stages. Two closely connected modules were identified to significantly classify patients into a low-risk group and high-risk group with different clinical outcomes. TF motif searching analysis suggested that TFs, such as NFAT5, might bind to the promoter and enhancer regions of hub lncRNAs and function in breast cancer biology. The results demonstrated that the synergistic, competitive lncRNA-TF ceRNA network and their functional modules played important roles in the biological processes and molecular functions of breast cancer.